Data collection vs integration: the Feasibility Study places too much emphasis on data collection, although most of the data is already available - and often 95% of the data is never used. We would like to offer to release thousands of Pillars 1-3 indicators with full data maps, description and with open source data integration code for beta-testing an Observatory, and gaining more feasibility insights of use, data quality, missing data, cost, cost/benefit analysis.
Innovation & information: CEEMID was born out of necessity to support similar non-data collection aims as foreseen EMO. Given that we believe that the availability is far better than the Feasibility Study states, and we would be happy to release a beta version of Pillars 1-3, more weight should be given to this part.
Feasibilty contains our recommendations regarding the Feasibility Study. Because we believe that data availability is far better than presented by the draft Study, we transferring our know-how in finding data, data standards and requirements.
Pillars: We shortly introduce what could experience, cost/benefit analysis, data maps, data assets, open source data integration code could be transferred from CEEMID to the Feasibility Study, a beta-test version of the Observatory services in Pillars 1-3 and how could further feasibility efforts focus on Pillar 4: Innovation.
In each topic you can go deeper with pressing ⯆ on your keyboard, touchpad or on the screen - we offer more arguments, examples, visualizations. You can jump to the next main topic with the ⯈ arrow on your keyboard or on the screen or by clicking the highlighted blue text. The first topic our disagreement with the ⯈ Data Collection priority of the study. ⯈⯈ Contact & More Info is at the end of the presentation.
We believe that the Feasibility Study is based on a false premise of lacking data and puts too much emphasis in data collection. We believe that most data is available for Pillar 1 & Pillar 2, and the industry is making huge steps in Pillar 3. We have integrated thousands of indicators on EU28 and recently even on regional levels, and we believe that the Feasibility of an EMO could be demonstrated by granting public access via a web application to CEEMID.
CEEMID’s thousands of Pillars 1-3 indicators were created to allow small-country stakeholders and small stakeholders without significant market research, R&D, IT capacities to use all tools mentioned in ⯈ Pillar 4: Innovation and aims to ⯈ inform policymakers. More on our ⯆ geographical coverage of Europe and ⯆ re-use of data
We would like to publicly release in 2020 thousands of Pillar 1-3 data for all EU countries with up to 20 years of history, with proper documentation and a fully open source integration code, so that the evaluation of the an Observatory’s feasibility can be based on real user experience, on real data integration and collection costs.
We have offered the Consultants a pro-bono consultation and we are ready to transfer much of the documentation, or data maps to thousands of indicators, existing collection, cost, use experience within the ⯈⯈ Feasibility Study .
The Feasibility Study states: Present a relevant and useful data collection framework covering the entire European music landscape, to help music sector operators and citizens better understand and benefit from this complex ecosystem
We believe that CEEMID has created much of the data collection framework. The problem is that it is financed from small contribution of small stakeholders, who do not want to open the system up to avoid free-riding and the tragedy of the commons, i.e. that nobody will eventually contribute to these costs. With a better funding model a music observatory would be already in place for all European stakeholders.
The current Open Data regime of the EU (formerly known public-sector re-use) allows access to publicly funded data collected for tax, statistical and research purposes, and it enables us to integrate from existing sources about 60% of the envisioned European Music Observatory ⯆ see our geographical coverage and observations on ⯆ data quality below. We are happily transferring this know-how to the EMO feasibility team.
CEEMID, while it also collects data to fill in some gaps in the existing data, is mainly a data integration framework that could produces thousands of indicators covering the ‘four pillars’ from existing European data sources. In our experience, most data exists, and about 95% of the data is never really used, partly because it is never displayed in a publicly available Observatory.
Review ⯆ geographical coverage, data quality and remarks below or continue to ⯈ system aims.
CEEMID started as the Central and Eastern European Music Industry Databases, and one of its aims was to bring these seemingly data-poor areas on an equal footing with their competitors, partners and users on the EU Single Market. We have always focused on finding CEE data and compare it with preferably EU-28 data.
CEEMID is not focusing on the CEE in data collection, on the contrary: it collected data in data rich Western and Nordic countries, and out of necessity created the data on less covered CEE (and often, Southern) member states.
CEEMID as a default covers the EU28 on national, and in some cases, NUTS2 regional levels. However, CEEMID makes efforts to include EEA countries Norway, Lichtenstein, Iceland, Switzerland; future and current candidate countries Albania, North Macedonia, Serbia, Turkey; and neighborhood countries Armenia, Ukraine, Georgia.
CEEMID like statistical authorities usually overcollects data. There are two reasons for this: often adding elements, questions to a data collection procedure is almost costless, because the fixed costs are high. Also, if data later will be needed, it is not possible to collect 2013 missing data in 2019.
The Open Data regime makes data abundant, and often free. However, this data is often semi-processed at most and not tidy, which means that very significant resources are needed to make it usable. This data processing know-how is often missing from microenterprise-sized music industry stakeholders.
We believe that the often cited example of the European Audiovisual Observatory is not very good, because this observatory was born before the EU, automated data processing and distribution, and the open data regime of the EU. CEEMID currently offers much higher data quality than the European Audiovisual Observatory (website) because it was designed under these circumstances. Creating the European Music Observatory, if technological and regulatory improvements are considered, are a much cheaper, simpler mercerize than the audiovisual observatory’s example would suggest.
We would recommend a high-level documentation of the data sources we used to create the Pillars 1-3, the experience with user needs, costs, willingness to pay, so that further efforts and research competition can focus on the real value added Pillar 4: Innovation.
CEEMID’s data are used in AI algorithm training for concert and record promotion, in pricing the use of music in various services, in designing better royalty collection and monitoring routines, in creating hard ex ante and ex post grant evaluation indicators. The only Pillar 4 activity where all data is not used is blockchain.
CEEMID’s Pillars 1-2-3 were born out of necessity: CEE stakeholders were disadvantaged versus rich market stakeholders because of data asymmetries
The competition authorities and EU Courts require the same level of data coverage in all EU countries that is public available only in a few EU member states
CEE stakeholders only receive 30-50% of their fair royalties because of the information asymmetries versus large users and platforms, such as Alphabet’s YouTube.
The Feasibility Study states: Provide a system delivering genuine European added-value, designed to generate the relevant level of new data to inform policy-makers and built to avoid overlaps with existing public policy or funding tools.
We do not believe that EMO system aim should be to “generate the relevant level of new data to inform policy-makers.” We believe that 95% of the existing data is not used, because of a lacking public funding model, and an open data integration that we would recommend. ⯆ Data collection vs integration
CEEMID has proven that even in seemingly data-poor countries there is an abundance of data present, but the sector is lacking research and IT capacities to present them to policy-makers.
Instead of generating new data, better quality data and high level information should be delivered. In most cases, the data is already there and it is disused, even though it is collected at great cost.
CEEMID in itself proves the need for an Observatory, because it was created out of necessity and without any public funds available. However, it would be extremely cost-effective and give the industry a huge boost if CEEMID would be publicly financed and would be available for all music stakeholders in all EU countries, and not only for paying users.
The Feasibility Study should be amended with a comprehensive map of existing EU-wide data collection schemes, and clearly indicate that these sources are available via the ⯆ Open Data Directive — ⯆ Example 1-2. Furthermore, we believe that the emphasis from data collection must switch in the study to data integration, because most of the data is already collected.
The Feasibility Study should clearly state of CEEMID’s existence, the fact that it covers in a ⯆ reproducible manner almost all the identified potential uses and data of Pillars 1-2, and making a lot of progress with Pillar 3 ⯈ See Pillars 1-4. This is a proof in itself that the creation of the European Music Observatory is possible even without legislative action and public funds, however, releasing it for the public would vastly increase its use all over Europe.
We would like to explicitly spell out in the Feasibility Study that CEEMID would like to ⯆ release for the public data history up to 20 years for thousands of indicators, or alternatively the software code that produces CEEMID itself automatically with a public grant that would create either a web application to document and retrieve the data, or a well-documented open source code. This could be a beta version of the EMO with demo tools for ⯈ Pillar 4: Innovation, to better understand the missing elements, costs and benefits of running a European Music Observatory.
The Feasibility Study should review CEEMID’s and other similar initiatives, for example, bmat’s or MusiMaps’ experience with innovation, to make it clearer that music stakeholders are not facing lack of data, but in the abundance of data they are missing skills and innovative tools to use them. Because data is abundant, emphasis should be made on reproducible research principles & open source development to ensure that the already existing data can far better be exploited.
CEEMID would like to transfer its know-how on data availability, data quality and data requirements to the Feasibility Study and to the future Observatory, or make it public for any interested party.
CEEMID adopted the Standard U.S. / EU data mapping of the music industry for emerging markets (see ⯈ Pillar 1 - our mapping in the entire value chain of the music industry), too, and provides a far more comprehensive data map, i.e. the US/EU standard Three Income Stream Model for collection, processing and creation of music industry indicators than the proposed Four Pillar Model.
CEEMID understands how to acquire ‘big data’ and other important data via APIs using open source software. We can release the open source code, or the acquired data after consultation with API owners as open data (see ⯈ use case in Pillar 2 - Music diversity and circulation).
CEEMID can provide know-how on the best practices of following ESSNet Culture guidelines - large pdf on the creation of music indicators (see ⯈ use case in Pillar 3: Music & Society),
CEEMID can provide the know-how how to adhere to the IFRS Fair Value Measurements principles that are essential to royalty pricing, benchmarking or factual grant evaluation premises
CEEMID follows the jurisprudence of the Court of Justice of the EU to provide stakeholders with indicators that are admissible as economic evidence, for example in pricing and royalty negotiation disputes, or damage claims (see ⯈ Use case in Pillar 4: Innovation).
The re-use of public sector information and various open data initiatives made it possible that we integrated many existing, publicly funded, EU-wide data collections without legal hurdles and with very little cost into thousands of indicators that cover all Europe in Pillars 1-3.
inflation data is made from a vast array of price data collected in EU member states; CEEMID re-uses the price data of cultural goods and services.
national accounts data is mainly created from the tax and mandatory statistical filings of creative industries; CEEMID uses
cultural access and participation data are sometimes included in EU research programs,
music education and training data.
The following indicator, which can be expressed for various target groups, is created solely from available Eurostat data.
CEEMID has been doing what Eurostat is doing, often years earlier: revisiting newly available open data sources, and creating new regional and national data products.
⎘ Zoom into the presentation online ⎘ read more: Creating better national cultural statistics with Eurobarometer datasets and ESSNet-Culture technical recommendations
Due to the effective EU Open Data regime, the bulk of CEEMID is mapped & integrated data from existing public sector data collections. The really valuable parts are, however, proprietary and business data elements in combination with and in integration with public sector data.
We do not redistribute confidential industry data, such as IFPI or CISAC data, however, we fully very comprehensively mapped that data. The metadata and CEEMID software code allows users, for example, the national member of these organizations, to immediately create clean, euro or national currency based indicators jointly with thousands of reliable open statistical and other data.
We do harvest hundreds of statistical indicators, but we do not only compile public statistics. We access raw statistical data, and create new statistics that is not available from Eurostat.
We not only clean and tidy public statistical tables, but we integrate them with approximated, forecasted and backcasted values for a more timely and complete international comparison.
CEEMID operates on reproducible research standards. This means that CEEMID’s tables, charts, models can be refreshed from the original source automatically. New annual, quarterly data is always immediately added. Data corrections of public sector bodies is always up to date.
CEEMID automatically makes currency translations (from dollar to euro, or euro to national currency units), unit conversions (euros, thousands of euros, and millions of euros), and other common transformations (per capita values, full-time equivalent values). Because small music industry stakeholders do not employ data scientists, this saves literally many hundreds of hours of work.
CEEMID’s reproducible research principle is a roboust solution to lack of data ownership in case of proprietary data. Whenever the original data is not publicly, the inclusion of such data is immediate whenever the data owner approves it.
CEEMID’s reproducible research principle is a guarantee of higher data quality than with the product of the European Audiovisual Observatory.
CEEMID uses the statistical programming language R, which is often used by national statistical services.
CEEMID is using only open source software, which keeps or innovative, Pillar 4 products reproducible, re-usable without further costs.
CEEMID itself contributes to rOpenGov, and releases some of its own software code open sources. We would like to find a public grant to release all ⯈ Pillar 1-3 data collection, cleaning and integration code and an open source license, so that music stakeholders, or the future European Music Observatory can immediately benefit from it. [This is less user friendly, and may be costly because of vast documentation needs.]
Another solution would be the creation of an Open Data web application, which would not release the software code, but all the data for free. [This is more user-friendly]
In either case, already existing data pioneers could use thousands of European indicators for the ⯈ Pillar 4 innovative products and serves.
A large part of the European Music Observatory data coverage is already available, but not well processed. One of the important causes of very low data utilization is that often very significant resources are needed to process the data.
Currency translations: we always use the correct exchange rates and translation methods among global (dollar), EU (euro) and national (various currency data). We quote growth on a euro/euro, dollar/dollar, national currency unit / national currency unit basis.
Unit conversions: making sure that euros, thousands of euros, millions of euros, revenue per capita or revenue per thousand people is always consistently used.
Regional data: In the past 20 years, intra-EU regions changed 5 times. We re-compute regional statistical indicators to follow the changes of national regions to allow a better time-series, panel or cross-sectional analysis of a wide range of indicators on subnational level.
Tidy data tables: we use the tidy data concept from statistical software design and data science to make sure that our thousands of indicators can be combined. Joining ill-formatted data usually takes up to 80% of user work hours to correct.
Macro-economic patterns and trends (e.g. employment, revenue): CEEMID over 5 years mapped data sources and create open source software to integrate thousands of indicators from EU research archives, Eurostat & ECB data warehouses and other sources. We are analyzing trends with automated reproducible research techniques, and many of our indicators are forecasted for 5 years.
Value chain mapping and analysis (e.g. characteristics of music companies, copyright collection, remuneration of artists, spill-over effects): We started building CEEMID with adopting the US & EU Value-chain mapping to less developed markets, so that we can analyse the value chain in any EU country. The value chain analysis is our standard method to create national music development programs (Czechia, Hungary, Slovakia), or detailed policy and ex ante grant evaluations. Use Case: Income-breakup of Musicians in 9 Countries. In 2019 we started experimenting with expanding our model to even less developed neighborhood and potential candidate countries, such as Bosnia-Herzegovina, Kosovo, Moldova & Armenia.
Legal models (e.g. tax, labour laws, social security, contracts). In the Legal models we believe that the comparative legal analysis requires so different data, or rather information acquisition and integration strategies that we concentrate only on numeric, measurable information, such as tax levels and tax burdens. While our rich data and comprehensive understanding of the value chain was used to advocate changes in laws and model contracts, this may be the only aspect of the proposed Four Pillars that is not well covered in CEEMID.
⯆ Pillar 1 examples & visualizations below; ⯈Pillars 2-4 to the right.
CEEMID uses and re-uses national accounts data (mainly derived from tax returns in member states), financial accounts data, industry collections, own collection, various semi-finished statistical products, and micro-data accessed via the OpenData regime.
The data availability is very good in Europe. However, it took 5 years for CEEMID to map sources and write programs to automate the collections of thousands of national level indicators.
This year we started to the creation of subnational and city-level indicators, too.
Understanding trends requires strong econometric skills and long data series. Many CEEMID indicators have 20 years or longer history, and they are forecasted programatically up to 5 years ahead.
We believe that one of the biggest economic challenges of the live music industry is seasonality, which creates huge financial risk. It makes capacity utilization of the cultural infrastructure and venues extremely challenging, especially farther away from the big European metropolitan centres.
We are also experimenting with short-term forecasting on ‘big data’ sources, for example, by comparing actual concert ticket sales with Google search data for concerts.
we mapped the EU music industry based on the Three Income Streams model (pdf), a standard US model adopted for Europe by the JRC Institute for Prospective Technological Studies.
This example is taken from the Mapping the Croatian Music Industry.
Our mapping is necessary to design data integration and primary data collection schemes. CEEMID contains ⯆ transaction quantity and price data from all mapped parts of the value chain.
With the help of about 100 stakeholders, we created the largest database of music professional skills, royalty earning assets, working practices and earnings.
Because the average enterprise size is less than 2 people and most musicians, technicians and managers work as freelancers, it is extremely important to collect data from all parts of the value chain. The microenterprises are usually exempted from mandatory statistical data filings, and make simplified tax returns and financial reports. Without surveying them individually, their economic activities remain hidden.
In the Legal models we believe that the comparative legal analysis requires so different data, or rather information acquisition and integration strategies that we concentrate only on numeric, measurable information, such as tax levels and tax burdens. While our rich data and comprehensive understanding of the value chain was used to advocate changes in laws and model contracts, this may be the only aspect of the proposed Four Pillars that is not well covered in CEEMID.
⯇ Pillar 1 Review (go back) to Pillar 1 or continue with ⯈ Pillar 2 Music diversity and circulation.
Cross-border circulation of works/repertoire (e.g. building common definition and indicators, mapping of cross-border access, sales and consumption flows) See next slide
Cross-border mobility of artists and professionals (e.g cross-border live performances, mobility of professionals, international music events)
Cultural diversity aspects (e.g languages, genres, types of productions)
CEEMID collects indicators on all of these aspects, and depending on the topic covers more or less EU countries.
We are currently working with many stakeholders to access grants to further develop both our data collection/integration methodology and provide better sampling of large quantity information (for example, circulation in streaming).
⯆ Pillar 2 examples & visualizations below. ⯇ Pillar 1 — ⯈ Pillars 3-4
CEEMID is working with author/publishing-side , producer/performer/recording-side and live music stakeholders to measure the cross-border circulation of works.
The music industry was among the first industries that went almost entirely digital and where almost all sales were robotized. The industry is characterized by vast amounts of digital data that are exploited by AI algorithms.
Data standardization is just catching up with this extraordinary change, and we are constantly building new tools to automate data collection and integration for all three income streams, i.e. publishing/authors, recording i.e. performers/producers, and the live music stream. We believe that our experience is could be extremely useful in designing and creating a feasible European Music Observatory.
CEEMID has been surveying musicians about their tour destinations since 2015.
In several countries our musician surveys are fully representative of the full-time and part-time musician populations, and we have representative touring information.
We are searching for new ways to reconcile this data with collective management data, because author societies technically should have information about foreign tours.
CEEMID has been measuring with surveys the use of various languages in live music, and we are experimenting with various representative ways with collective management societies to measure language use in radio and streaming channels.
We are currently working on a large case study to understand the effect of national radio quotas, and what can be there lessons for better public broadcaster programming and in streaming.
We see an extremely fast shrinking market share of domestic and small language works. Because most music is sold by AI algorithms in the world (both in streaming services, and increasingly in radios and background music), small country repertoires are extremely disadvantages, as they do not have large enough datasets and local resources to train better algorithms.
Education, training, personal development: We believe that in the majority of the member states the biggest development obstacle in the music industry is the lack of access to life-long learning for music professionals. Audience building in many member states is poor because of obsolete or low level of general education and music education activities of future audiences (see ⯆ education).
Audiences (music consumption, interaction, participation to music events, etc.) This topic is very well covered in CEEMID, because we had been providing a best practice of the ESSNet-Culture Technical Guidelines on measuring market-based (ticketed, licensed) and non-market (liturgical, home copying, illegal, etc.) forms of participation in the music scene. See Audiences and our ⯆ Use case: demography comparison of the EU28 concert audiences. We show also or AI model from the following, ⯈ Innovation pillar to predict the likelihood of concert visits for various European people.
Music and society (not-for-profit sector, associations, social inclusion, amateur music, heritage) This topic is very well covered in the CEE countries, where we have collected primary data, but partially covered from existing, re-usable EU data sources, too. (see ⯆ more)
Navigate to back to ⯇ Pillars 1-2 or forward to ⯈ Pillar 4. ⯆ Pillar 3 Music Society & Citizenship examples & visualizations below.
We have been collecting education and training data, and needs data from musicians, technicians and managers since 2015. Our CAP surveys (see ⯆ next slides) contain audience music education variables.
In our grant assessment workshops and surveys, music education and professional development gets the top priority from musicians, technicians and managers. Because about 95% of the industry works in microenterprises, and the rest in small enterprises, strategic HR functions, such as skills assessment, life-long learning are missing in most countries and can be designed only by national stakeholders.
The most important revenue source of CEEMID is pricing music and setting royalty levels. According to the international copyright law, the price of music should be based in many uses on the value in use.
We are collecting data on how music is built into the value chain of restaurants, shops, hotels, which includes data on how people perceive music in this scenes of public performance.
We are collecting data on how people use music from licensed, exempted (home copying) and illegal sources.
Data availability is not bad, but in some member states data is available only with little frequency. The data availability is not enough in many countries to find accurate answers to current issues, for example, to understand the actual value transfer from rightsholders to media platforms. In these cases, we are designing and implementing new data collection following the technical guidelines of ESSNet Culture.
In most countries, Europeans visit less and less concerts as they are ageing. In all countries it is starting from the teenager group of 15-18 at 2-3 concerts per year. The visiting frequency drops to 1 concerts in every 2-3 years in the 40-45 age group. Especially in the CEE, this results in a very young concert and poor audience because the higher earning middle-aged people are absent from the venues.
Music and society (not-for-profit sector, associations, social inclusion, amateur music, heritage)
CEEMID’s music professional surveys have been collecting membership data in associations and non-profit organizations, such as collective management societies since 2014.
CEEMID’s CAP surveys, and our re-used surveys (see for example ⯇ previous slide on the survey, and a use case in ⯈ next Pillar 4 technological evolution - A.I.) contain indicators on amateur music practices in the general public in all 28 EU member states.
Technological evolutions (e.g AI, blockchain)
Future business models (e.g distribution platforms, branding, monetisation, fair remuneration, authors rights collection mechanisms)
New policies and support schemes (policy “think-tank” department)
In fact, these are CEEMID’s main aims, and all the 3 previous pillars had to be created to fulfill this need. We believe that Pillar 1, Pillar 2, Pillar 3 was a necessity to start building the real value creating activities of this Pillar.
⯇ Pillars 1-3 - ⯈Contact & More Information - ⯆ Examples & visualizations below
CEEMID was created with the aim to provide state-of-art AI/machine learning, analytics products in the geographical areas and industry areas where it is most needed: in less developed EU countries and for the majority of artists who are not represented by labels and publishers with such technologies.
The example shows a comparison of the Czech and general EU audience model which predicts who will likely visit at least one concert in the coming year.
CEEMID is constantly seeking ways to access DSP and other industry data from application program interfaces (APIs) and follows ESSNet best practices to use big data sources.
The following chart that covers the streaming quantity growth of a typical song in 20 countries was created as an experimental
CEEMID has used all recognized music / intellectual property valuation methods in the music industry.
We use the ‘music comparators’ model to evaluate to value or private copying, the euro value of the value transfer to media platforms, and to assess the royalties paid by radios and television stations.
We use ‘hedonic price models’ to valuate various uses, mainly background music uses in restaurants, hotels and shops.
We use ‘econometric models’ to objectively justify social, economic and cultural differences that must be taken into consideration when pricing music, following the jurisprudence of the Court of the European Union, for example, in the AKKA/LAA vs Konkurences padome case and the Léčebné lázně Mariánské Lázně v OSA case.
We use hypothetical evaluations where no market data is available, for example, to assess the viability of new products, services, or to make reality checks on the other models.
CEEMID compiles indicators from European CAP surveys accessed via the Open Data regime and creates new CAP surveys following the technical guidelines of ESSNet Culture.
The following example is a randomized example from one of our actual surveys about how people listen to music at home, while they travel and at work. These indicators are essential to use any market comparator models, and they are often required in royalty disputes.
CEEMID compiles proprietary data that it never owns, via its data integration model and metadata based on mapping industry sources to compare the actual use of music with actual royalty payments. When such information is not available, we rely on our CEEMID Music Professional Surveys, which contain the earnings data of thousands of European musicians.
The following example is a randomized example that resembles the true values. The unknown quantity is the value of not licensed music, i.e. private copying and value transferred to YouTube or other media platforms. This is a valuation method used by IFPI. The CEEMID market comparator model is a more detailed version of the valuation model created for IFPI by PWC.
The following, again randomized example shows suggested private copying remuneration and value gap correction levels with two scenarios with different input parameters. The appropriate level of compensation and target revenue can be compared with actual royalty income.
Music stakeholders need such data when they are in royalty disputes, in litigation, or when they are making regulatory impact assessments. We believe that this information is critical for the correct transposition of the new DSM Directive, too. This example, while eventually results in four values : PCR compensation euro value A, B, and target media platform revenue A, B, relies on the use of dozens of indicators which comply with ESSNet Culture statistical technical recommendations and IFRS Fair Valuation principles. (YouTube is only highlighted because it is far the largest platform, and we emphasis that while we have made real calculations, this is just a randomized number example and not the basis of any claim against YouTube.)
New policies and support schemes (policy “think-tank” department)
Advocating for Small Venues: we have shown that the loss of small venues in some areas were up to 60-70% in the world economic crisis. Our research shows what are the missing skills, regulatory problems, technology issues with re-opening these grassroots locations of the live music industry.
Advocating for Better Taxation: our economic impact analysis software, which is partly open source, showed that in Slovakia, the music industry currently pays up to 100x more tax than the politically favored car manufacturing. Our model was also used to make calculations for the Hungarian film tax shelter.
Advocating for Closing the Value Gap: we have calculated the euro value and the mechanism of value transfer from rightsholders to YouTube in two countries, and we are continuing with further countries.
Hungarian Music Industry Report — Slovak Music Industry Report — Made in Hungary
Contact: danielantal.eu - linkedin - github - dataverse
Topics: royalty valuation - private copying - grant design - policy advocacy - ceemid observatory
In this presentation Open data was used with open source software components. Detailed references in long-form publications for all software code used.
The European statistical maps: © EuroGeographics for the administrative boundaries
Visualization: © Daniel Antal, CEEMID, 2019.